{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-5.0</td>\n",
       "      <td>25.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-4.9</td>\n",
       "      <td>24.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-4.8</td>\n",
       "      <td>23.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-4.7</td>\n",
       "      <td>22.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-4.6</td>\n",
       "      <td>21.16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     x      y\n",
       "0 -5.0  25.00\n",
       "1 -4.9  24.01\n",
       "2 -4.8  23.04\n",
       "3 -4.7  22.09\n",
       "4 -4.6  21.16"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "data = pd.read_csv('transfer_data.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data.loc[:,'x']\n",
    "y = data.loc[:,'y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "fig1 = plt.figure(figsize=(7,5))\n",
    "plt.plot(x,y,marker='o')\n",
    "plt.title('y vs x')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array(x).reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_6 (Dense)              (None, 50)                100       \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 50)                2550      \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 1)                 51        \n",
      "=================================================================\n",
      "Total params: 2,701\n",
      "Trainable params: 2,701\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "model = Sequential()\n",
    "model.add(Dense(units = 50, input_dim = 1, activation='relu'))\n",
    "model.add(Dense(units = 50, activation='relu'))\n",
    "model.add(Dense(units = 1, activation='linear'))\n",
    "model.compile(optimizer='adam',loss='mean_squared_error')\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
      "101/101 [==============================] - 0s 1ms/step - loss: 124.8944\n",
      "Epoch 2/1000\n",
      "101/101 [==============================] - 0s 119us/step - loss: 119.2876\n",
      "Epoch 3/1000\n",
      "101/101 [==============================] - 0s 119us/step - loss: 114.3304\n",
      "Epoch 4/1000\n",
      "101/101 [==============================] - 0s 109us/step - loss: 109.2686\n",
      "Epoch 5/1000\n",
      "101/101 [==============================] - 0s 119us/step - loss: 104.5772\n",
      "Epoch 6/1000\n",
      "101/101 [==============================] - 0s 109us/step - loss: 99.9734\n",
      "Epoch 7/1000\n",
      "101/101 [==============================] - 0s 99us/step - loss: 95.6395\n",
      "Epoch 8/1000\n",
      "101/101 [==============================] - 0s 119us/step - loss: 91.1571\n",
      "Epoch 9/1000\n",
      "101/101 [==============================] - 0s 99us/step - loss: 86.5007\n",
      "Epoch 10/1000\n",
      "101/101 [==============================] - 0s 109us/step - loss: 81.5329\n",
      "Epoch 11/1000\n",
      "101/101 [==============================] - 0s 119us/step - loss: 76.6628\n",
      "Epoch 12/1000\n",
      "101/101 [==============================] - 0s 109us/step - loss: 71.3121\n",
      "Epoch 13/1000\n",
      "101/101 [==============================] - 0s 99us/step - loss: 66.2356\n",
      "Epoch 14/1000\n",
      "101/101 [==============================] - 0s 99us/step - loss: 61.2069\n",
      "Epoch 15/1000\n",
      "101/101 [==============================] - 0s 119us/step - loss: 55.9761\n",
      "Epoch 16/1000\n",
      "101/101 [==============================] - 0s 109us/step - loss: 50.5369\n",
      "Epoch 17/1000\n",
      "101/101 [==============================] - 0s 99us/step - loss: 45.6132\n",
      "Epoch 18/1000\n",
      "101/101 [==============================] - 0s 109us/step - loss: 40.7396\n",
      "Epoch 19/1000\n",
      "101/101 [==============================] - 0s 109us/step - loss: 36.3591\n",
      "Epoch 20/1000\n",
      "101/101 [==============================] - 0s 109us/step - loss: 32.6934\n",
      "Epoch 21/1000\n",
      "101/101 [==============================] - 0s 99us/step - loss: 29.5371\n",
      "Epoch 22/1000\n",
      "101/101 [==============================] - 0s 109us/step - loss: 26.5466\n",
      "Epoch 23/1000\n",
      "101/101 [==============================] - 0s 99us/step - loss: 24.3525\n",
      "Epoch 24/1000\n",
      "101/101 [==============================] - 0s 109us/step - loss: 22.1209\n",
      "Epoch 25/1000\n",
      "101/101 [==============================] - 0s 99us/step - loss: 20.1934\n",
      "Epoch 26/1000\n",
      "101/101 [==============================] - 0s 89us/step - loss: 18.6164\n",
      "Epoch 27/1000\n",
      "101/101 [==============================] - 0s 119us/step - loss: 17.2219\n",
      "Epoch 28/1000\n",
      "101/101 [==============================] - 0s 109us/step - loss: 15.9208\n",
      "Epoch 29/1000\n",
      "101/101 [==============================] - 0s 79us/step - loss: 14.8153\n",
      "Epoch 30/1000\n",
      "101/101 [==============================] - 0s 99us/step - loss: 14.0472\n",
      "Epoch 31/1000\n",
      "101/101 [==============================] - 0s 99us/step - loss: 13.2037\n",
      "Epoch 32/1000\n",
      "101/101 [==============================] - 0s 99us/step - loss: 12.7144\n",
      "Epoch 33/1000\n",
      "101/101 [==============================] - 0s 79us/step - loss: 12.1830\n",
      "Epoch 34/1000\n",
      "101/101 [==============================] - 0s 89us/step - loss: 11.7852\n",
      "Epoch 35/1000\n",
      "101/101 [==============================] - 0s 99us/step - loss: 11.5308\n",
      "Epoch 36/1000\n",
      "101/101 [==============================] - 0s 89us/step - loss: 11.2640\n",
      "Epoch 37/1000\n",
      "101/101 [==============================] - 0s 89us/step - loss: 11.1390\n",
      "Epoch 38/1000\n",
      "101/101 [==============================] - 0s 99us/step - loss: 10.9862\n",
      "Epoch 39/1000\n",
      "101/101 [==============================] - 0s 99us/step - loss: 10.8563\n",
      "Epoch 40/1000\n",
      "101/101 [==============================] - 0s 89us/step - loss: 10.7394\n",
      "Epoch 41/1000\n",
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      "Epoch 949/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "101/101 [==============================] - 0s 89us/step - loss: 0.0026\n",
      "Epoch 950/1000\n",
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      "Epoch 995/1000\n",
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      "Epoch 996/1000\n",
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      "Epoch 997/1000\n",
      "101/101 [==============================] - 0s 89us/step - loss: 0.0017\n",
      "Epoch 998/1000\n",
      "101/101 [==============================] - 0s 69us/step - loss: 0.0018\n",
      "Epoch 999/1000\n",
      "101/101 [==============================] - 0s 79us/step - loss: 0.0018\n",
      "Epoch 1000/1000\n",
      "101/101 [==============================] - 0s 79us/step - loss: 0.0016\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x1894e70ef98>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x,y,epochs=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = model.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4Ki6pvZN7ej5IBT2c9cFLmDdjUtChiYgMKN65FzNo48iSbcpOpwbtPJk3YxLzZkxi3ZoaRi77Gcdufgg4LeiwRET6dbC7mykvP8D6kdM45fpfAZRkU3Y6XbHl2TtPezev0sC09bdpE1IRCa3UZqKVXzqKybzGvjFvDzqkvFFiy7NVP15Kg7dTZXFiltiEtJT7QUQkelJ9aw20HerBPXXH8sicp5TY8qyxtYkR1t1rrJT7QUQkeqLWt5ZJiS3PotYPIiLRE/XzlBJbnkWtH0REoifq5ykltjzTJqQiEnZR2Ey0P0pseZa+CWmq4XH1kReUbD+IiETP9AuvYKfV0eUVxJ2S71vLpD62AkhtQurxOK/eMo3xe9fh8TgW098RIhK8Vf/9IO9hJ+vOuJV3Xfh3Jd+3lkmJrYAsFqP9XZ9g5jNfon3x8Yzz3eywejbNXBiZv4xEpHQ0L19CY2sT7/Y2ejA6D/QEHVJB6BKiwHqsCncYz271tYlIYDJ71yrMedeamyN5LlJiK7DGtbf32YQ0Sv0iIlIaot67lk6JrcCi3i8iIqWhnM5FSmwFFvV+EREpDeV0LlJiKzD1tYlIGLwy7RriEdpzrT9KbAWW2dd20GOsnXGzqiJFpKh6PLEw+07GRGLPtf6o3L8IUn1tzzx6N6f+5nMw8oigQxKRMhLvidOw7k5erpjClC+uwWKxyPWupdMVWxG969yPs4vRTH/qGu3VJiIFl9pzzRYfyQnxV3itbnpZLBQR/d8wRFp+cjejvJOR1q2eNhEpqGx7rk17/ZGyON8osRVRYq+23p3+Ue0jEZFglVPfWiYltiIqpz4SEQlWOZ9vlNiKqJz6SEQkWOV8vlFiKyL1tIlIsWyauZCDEd5zrT9KbEWU3tOWapRcc8T7I9lHIiLBesf7/pweYnT4yMj3rWVSH1uRpXraADZ+5T00vrGW/Qe6qB45YoDvFBEZvOcevp0z7CDPz3uYt03/o0j3rWXSFVuAes64ism8RsdXTlRfm4jkRaJ37UROf/429nsVu199LuiQiq5gic3M7jazHWb2bNrYIjPbYmZrkv8uLNTzl4K9e/cSdxjHXvW1ichhe6t3rR0zqLaDZXlOKeQV238A52cZv83dpyf//aSAzx96jWtuI6a92kQkT8q5dy1dwRKbuz8J7CzUz4+Ccu4zEZH80zklIYh7bJ82s7XJqcojA3j+0CjnPhMRyT+dUxKKndi+CZwITAe2AV/L9UAzm29mq8xsVVtb9r9CSp362kQkn16c8ud9xsrxnFLUxObur7l7j7vHgW8Bp/fz2KXuPtvdZ9fXZ/8rpNRl7tXmDs0nfaYs+kxEJP+qXt/Afq/iNY4qu961dEXtYzOzo919W/LTi4Fn+3t8OUj1te3Z/iK135wNO18KOiQRKUGvvLCeGfueoPWYj3H6gjsAyqp3LV3BEpuZfRc4GxhvZpuBm4CzzWw64MDLQHn9GdGPuoYT+P3IU3jvzh8Rv+lH7LB6Ns1cWHZ/aYnI0DQvX0JjaxPHJgtHvG5ywBEFr2CJzd0/lmX4rkI9X6lrXr6EUw9sOFT+30AbdS030AxKbiKSVapvrca6ILXn2vrbaF5+VFmfN7TySEg0tjZRbQd7jZVj/4mIDJ761rJTYgsJ9Z+IyFDpvJGdEltIqP9ERIZK543slNhCIltPW5dXlF3/iYgM3sunfgb33mPl2LeWSYktJHrv1WYc8ErepJqp5/110KGJSEh1738TM3idurLuW8tknpnuQ2j27Nm+atWqoMMoqg0rH+Tkn3+C3YxmjL+h8n8ROSRR4v9VJno7XVSydtaXmTP3b4MOq+jMrMXdZ2eO64otpPbufI24G2N5Q1vaiMghmVvTjLRuprb8X50b0iixhVRj6z8Ts95X0yrjFRGV+A9MiS2kVMYrItno3DAwJbaQUhmviGST6xygc8NblNhCSlvaiEg2G4/64z5jOjf0psQWUplb2gA8NfGjqooUKWM9PXHqd7XyOnVsZ7xK/HMo6rY1MjSpLW3iBzrY+ZVTqNq+huOvW8ExY2tY+KGTmTdjUtAhikgRLFu9haZHN3DS3qe4Z8QLPDTlei765HVA+W5N0x8lthKw/LldVMRP4U9iv+GFkZeytWM8X3/wL4GrlNxEIm7Z6i2sfPAO7ud7TKpqp9tj/Pql3fjqLTr+c9BUZAlYs2IpH7AWAGIGk2PtLLalrFmxNODIRKTQ1qxYymJbyuRYom+t0uLcZHfp+O+HElsJuKLr3j59K7XWxRVd9wYUkYgUyxVd91Kr439IlNhKwDGx14c0LiLRoeN/6JTYSsD+muy3hnONi0h0dOr4H7IBE5uZfdrMjixGMJJd7QWL6a6o7jXWg1F9/uKAIhKRYtk0+aI+Y90V1dReoOM/l8FcsTUAzWb2gJmdb2ZW6KAkw7RLqLzodqhrBIyuqjFU4Ox68AvEb6pj+6KTtACqSMQsW72FM299nJ2/f5I9XkNHdQNgUNeYOB9MuyToEENrUNvWJJPZB4FPArOBB4C73P2FwoaXUI7b1vRn1YO3M3PNDcTS/sTo9BFq0hSJiFSJ/z/Yf1Fve9jto7jFP8l7L1aLT7rD2rbGE9lve/JfN3Ak8AMz+2peo5RBmfy7b/RKaqDVvUWiJFXiX297ABhrb6rFZwgGc4/tajNrAb4K/A9wqrv/HTAL+LMCxydZaHVvkWhTif/hGczKI+OBP3X3V9IH3T1uZh8pTFjSnx1WTwN9k9sOG6+ldUQiQCX+h2fAKzZ3vzEzqaV9bX3+Q5KBaOV/kWjrqBiTdVwl/oOjPrYSlG3l/zV1H1DhiEgEdOw/wJs9MeL0vpGuEv/BG1RVZNBUFdkPd/7wlTOZeOBlOq2WCd7ODqtn08yFSnQiJaR5+RIaW5uY6G2YwYtjzuAE2wp7NkPdZDj3RpX4Z8hVFanV/UudGbsmnMHbNz9HHR1g0EAbdS030AxKbiIloHn5Eqa23JBYEzZ5oXb0ntU0q4VnWDQVGQFTNj/cZ0zl/yKlo7G1qc9C5zqGh0+JLQJU/i9S2nQM55cSWwTssPoc4+OLHImIDIeO4fxSYosAlf+LlLbnT76SzDo+HcPDp8QWAZnl/+6w+qS/101nkRLRtWMjDrRxJHE3tlOvtV8Pg8r9I2bn9lcZ9c3p9FBJNQdU+i8SUqny/gnehgF/qJ7Kydf/T9BhlZTDWgRZSscLv32EGFBrB4glS/+nttygbW1EQiRV3t9AGzEDM5iyf4OO0zxRYouYxtYmqqyn15jKhkXCJVt5f7Ud1HGaJ0psEaOyYZHw03FaWEpsEaOyYZHw03FaWEpsEZOt9L/LK1U2LBIiL0//HHHvvcixyvvzR2tFRsycuQtohkPVVt1U0kWMxtavEm+5VlWSIgFKVUKekVzo+E2vpoYD7LDxbJql4zJfVO4fcU/feQ1nbP52r7FOH6EeGZEi67XQcZKOxcNT9HJ/M7vbzHaY2bNpY+PM7DEzez753yML9fySoAWSRcJBCx0XTyHvsf0HcH7G2HXA4+7+NuDx5OdSQKq+EgkHHYvFU7DE5u5PAjszhi8C7kl+fA8wr1DPLwmqvhIJBx2LxVPsqsiJ7r4NIPnfCUV+/rKjBZJFwmHDMXP7jOlYLIzQlvub2XwzW2Vmq9rasl/Cy8AyF0gGuM8/xCW/nsxZt/6CZau3BBugSIQtW72Fs279BSdc92PqNv+SPV7LdsZroeMCK2hVpJkdBzzs7lOTn28Aznb3bWZ2NPCEu5880M9RVWR+PNj8Emf++P2Ms31UEGerj+fr/CXvvfgq5s2YFHR4IpGybPUWVj54B9fwPSZZO2bw3Z5zqfnT23W85UlYFkFeDlye/Phy4KEiP39Z+92j/8FYe5MqixMzmBxrZ7EtZc2KpUGHJhI5a1YsZbEtZXIskdQALor9SsdbERSy3P+7wG+Ak81ss5n9DXArcJ6ZPQ+cl/xciuSKrnsZad29xmqtiyu67g0oIpHouqLrXmozyvt1vBVHwVYecfeP5fjSuYV6TunfMbHXhzQuIsOn4y04oS0ekfzbX9MwpHERGT4db8FRYisjtRcspruiutdYD8b/67qE469boSpJkTxIVUL+176Zfb7WXVFN7QWLA4iqvCixlZNpl1B50e1Q1wgYHRVjqMC5uvtuXhh5Kfd3XMnKB+9QchMZplQl5AMdVzC/YgXdDrt8NI5BXWPi+Jt2SdBhRp4SW7mZdgl87llYtJsmPkHcjaNsn6okRfIgVQk5KfY6ZlBpMJIubq78bOK4U1IrCiW2MvY3B+8jZr37GFW1JTJ8qoQMByW2MqaqLZH80jEVDkpsZUxVWyL51VE5Nuu4jqniUmIrY9mqJN2hpmMb2xedRPPyJQFFJlI6mpcvYfuik4jfVEf1wV1kLlKoSsjiU2IrZ2lVkk4iqZkl/jXQxtSWG5TcRPqR2hW7gTZiyWKRbjcOVI0FVUIGRomt3CWrJF+j/tB6dina3Vekf9l2xa4yZ9fBKli0W5WQAVFiE0C7+4oMh46bcFJiE0C7+4oMh46bcFJiEyD7TtsHvUK7+4r044V3XkU8o1pEu2IHr2Cr+0tpmTN3Ac0k7hlM8HY6GUkN+zm+9Z+It1zLDqtn08yF2u1XhETRSGNrE2d6G2awjxpG+X522Hg2zdJxErSC7qCdL9pBu/h+88DXOGPdYmJpBSWdPkJb2UvZS1VCpheN6NgIRlh20JYScfxz3+yV1EBVkiKQvRJSx0a4KLFJVqr2EslOx0b4KbFJVqr2EsnudTsy67iOjfBQYpOsslVJusNEb9NyW1J20pfNOtJ3k1maoErIcFFik6zmzF3As7NuYTv1xF3LbUn56rtsltONsYsjiLuxnXoVjoSMqiJlQNsXnUQDfe8rbKeehkUbA4hIpHj0/g8vVUXKsOlmuZQzvf9LjxKbDEiFJFLO9P4vPUpsMqBshSTdHmPTjM8HFJFI8bw4+SIVi5QYJTYZUO9CEmMvtVRanJNWf5n4TXWqkpRISlRCnsh7Nt1JHNjNaBWLlAgVj8iQNS/7N2at/iIxe+u9oyWFJEq0bFZpUPGI5E3jmtt6JTXQkkISLVo2q7QpscmQqUpMok7v8dKmxCZDpioxiTJ3p5PqrF/Te7w0KLHJkGm5LYmi1LJZLBrLKNtPj/fe3kKVkKVDiU2GTMttSdSkL5tlyXzWQ0zLZpUoVUXKYdFyQxIFeh+XJlVFSkHoJrtEgd7H0aLEJoclVyFJ3AwWjYXbpsLaB4oclcjQtNm4rOMqFilNSmxyWLIVkgBUEgcc9myi+6HPKLlJ6GiPtehSYpPDkrncVneWt1Rlz346HrkxgOhEssvcY22ExenRHmuRURl0AFL65sxdAKkTwKKxWR9T3bm9iBGJ9C/byiKV5hygmtjNm2kAGoIJTfJAV2ySV1vjRw1pXCQIKhaJNiU2yas7R1xGR58tboxa26+dACRwqftqluPrKhaJBiU2yavpH57PjT6fzfHxxB26PEYFzjh7g5gauCVA2Zqw06lYJDoCSWxm9rKZPWNma8xMndcRMm/GJN578VV8tPZbnHjgPl5nbJ+TiFZJlyBku68GiZVzVCwSLUEWj/yxuya0o2jejEnMmzEJgPhNl2Z9jO5lSLFN8DayzUE6RsOijSoWiRBNRUpBaScACYt2NWGXjaASmwM/M7MWM5sfUAxSBNoJQIKU3oQ9znepCbtMBDUVeZa7bzWzCcBjZvZ7d38y/QHJhDcf4Nhjjw0iRsmDOXMX0Ezi/sYEb8Pg0D23Btqoa7mB5uTjRPIpVSxSY11gEMM56MYbjKbO32CHjWfTrIV670VQ4Kv7m9ki4A13/+dcj9Hq/tGgFdSlmPR+i77QrO5vZqPM7IjUx8AHgWeLHYcUn5pipZj0fitfQdxjmwisNLPfAb8FVrj7TwOIQ4os504AmJq3JW/UhC1Fv8fm7i8CpxX7eSV4m2YupC51zyNNpcUB3XOTw5d5Xy1Tp49g06yFKu2POJX7S9H02QnA+7791Lwth0NN2AJa3V+KrNdOADfVZX2M7oHIcKkJW0BXbBKgXPfcDNf9Nhm09F61XHRfrbwosUlgcu2+bVosWQYpc8PQmKEmbFFik+Ck33PL1k6p+26XodAAAAmrSURBVG0ykGz31Myg22PaCbuM6R6bBCp1zy1+U13W8mzdb5P+5LqnFsOJ3bxbO2GXKV2xSSiox02GQr1q0h8lNgmFXIslV1pcG5RKL9owVAaixCahkK3HTRuUSjbqVZOBBL4I8mBoEeTyE7+pjli2fiSH16yeTTO1Kns5WbZ6C02PbmDr7k5eGHlp1vdG3I3YzbuLH5wEJtciyCoekVDaYfVZV2ZPtQJo6a3ysWz1FlY+eAf38z2OGdne7301FYoIaCpSQipXj1uKpiXLx5oVS1lsS5kcaydmiT9u1Ksm/VFik1AaqMcN1ApQLq7oupda9arJEOgem4Rerg0juz1GjDg7dM8tkpqXL6GxtYmJnr36MY4RW6R7auUsNBuNigxVrmlJtQJE10Al/QD7a3RHTbJTYpPQ03Y35SdXSX9Kd0U1tRcsLmJEUko0FSklR60A0ZSaepzgbRhkvVJzBxvbCOfeCNMuKXqMEi4q95fIUCtA9Ay083XKa1ZPw+eeLV5gUpI0FSklR60A0TPQ1COopF8GT4lNSs5gWgEmepsWTy4BqcWMJ3rfK3BITD2qpF+GSvfYpKTlagVI1+kjdFIMoV7Tjzlsp56GRRuLGJWUEpX7SyQNNC0JmpoMq4GmHzX1KMOl4hEpaXPmLqAZktV07RietZouNTWpZu5gpVc+ToSshSKHqltn6f+TDI+mIiVSNDUZXoOZegRNP8rgaSpSyoKmJsNLlY9SLEpsEimZq5T0VzWpisnCS1U9xm+qU+WjFI2mIiXSBpqa1LRk4WjqUQpNU5FSlgbTzD2j5Tr1vOVR6iptdsu1mnqUQKgqUiItvWoy1/YnlRYHtBxXPgxmaSx3cIwdNl6Vj1IQmoqUsjGYiknQYspDtWz1Fpoe3cDW3Z2sHHk1k6z/DWA19Sj5oqlIKXuDqZiEtxZT1h5vA1u2egsrH7yD+zuu5IWRl3IM/Sc1TT1KMSixSdkYzL5u6Wqsixmt18GisXDbVFj7QJEiLR1rVixlsS1lcqydmGXfagYSV8GqepRi0VSklK3BVu2ldFdUU3nR7doHjLdWEMl13zKdKk+lUHJNRSqxSVkbygkayvf+W/pSWHtsNKN8PyOsO+fjexWIlNlrJcWjxCbSj6FevZXTVchQXxuAjpqjqf2H3xcwKhHtoC3Sr8zFlOPYoTaAbA71v7VcG9mFlVNXabMHeTWb0l1RTe0FiwsXmMgAVDwikjRn7gIaFm0kdvNubqm6mo4BKigrLU4sohWUqau0Bgaf1BygrlH3ISVwSmwiWUz/8Hxu9Plsjo/Pud5kuhrrYnbLtSW9ekn6uo4zWq4b0tRjp49g1cyvwueeVVKTwOkem0gOqcbjWXsf49aqO6kd5In+gFfQYbXU+b7QT1OmF4UAxAZ5dfbW7/iGCkQkMCoeETkMbyWAge+/ZYp7YnWpMFRTDrW6MVO5VoVKOCmxieTJcKoEU4pyNbf2AXh8MezZzIGqMXQejDPG9w0rkaUrp0pQKQ2hSmxmdj7wDaACuNPdb+3v8UpsEjZD7X/LJT3R7bHRgB1Kei+Ney/H71x56OpqMF/ba0cw2vZT6Qfz8nt2e4wYrulGCaXQJDYzqwD+AJwHbAaagY+5+3O5vkeJTcLqcK7eBuLe/xJVh5NQB0NXaBJ2YVoE+XRgo7u/6O5dwPeAiwKIQ+SwZa4/uYvRdHl+2kP7S1yFSmpx17qOUvqCaNCeBGxK+3wzcEbmg8xsPjAf4Nhjjy1OZCLDMGfuAkgmgCMZfqVhEPpUNyb3R2sAGoIOTmSYgkhs2Q7zPvOh7r4UWAqJqchCByWSL+mJLr2aco+NOqzijXzIlcgAJTOJjCAS22agMe3zycDWAOIQKbjcV3MDJ7rh3mM74BW8SQ1jeZNdPgozGMubSmRSNoIoHqkkUTxyLrCFRPHIpe6+Ltf3qHhEoioz0SWqGxNXU29VPg7ta08e+3d8Y8cMtu7u5JixNSz80MnMmzEp4N9UJP9CUxWZDOZC4Oskyv3vdvcv9/d4JTYREckUqtX93f0nwE+CeG4REYk2LYIsIiKRosQmIiKRosQmIiKRosQmIiKRosQmIiKRosQmIiKRosQmIiKRUhIbjZpZG/BK0HHk2XigPeggQkqvTW56bXLTa5NbVF+bKe5enzlYEoktisxsVbaOedFr0x+9Nrnptcmt3F4bTUWKiEikKLGJiEikKLEFZ2nQAYSYXpvc9Nrkptcmt7J6bXSPTUREIkVXbCIiEilKbCFgZl8wMzez8UHHEhZm1mRmvzeztWb2oJmNDTqmoJnZ+Wa2wcw2mtl1QccTFmbWaGb/bWbrzWydmX026JjCxswqzGy1mT0cdCzFoMQWMDNrBM4DXg06lpB5DJjq7tNI7Lh+fcDxBMrMKoB/Ay4A3gl8zMzeGWxUodENfN7dTwHeDfy9Xps+PgusDzqIYlFiC95twLWAbnamcfefuXt38tOngMlBxhMCpwMb3f1Fd+8CvgdcFHBMoeDu29y9NfnxPhIn8EnBRhUeZjYZ+DBwZ9CxFIsSW4DMbC6wxd1/F3QsIfcp4JGggwjYJGBT2ueb0cm7DzM7DpgBPB1sJKHydRJ/PMeDDqRYKoMOIOrM7OdAQ5YvfRH4R+CDxY0oPPp7bdz9oeRjvkhiquk7xYwthCzLmK7y05jZaOCHwDXuvjfoeMLAzD4C7HD3FjM7O+h4ikWJrcDc/QPZxs3sVOB44HdmBomptlYzO93dtxcxxMDkem1SzOxy4CPAua6+lM1AY9rnk4GtAcUSOmZWRSKpfcfdfxR0PCFyFjDXzC4EqoExZnavu18WcFwFpT62kDCzl4HZ7h7FhUqHzMzOB/4FeL+7twUdT9DMrJJEEc25wBagGbjU3dcFGlgIWOIvw3uAne5+TdDxhFXyiu0L7v6RoGMpNN1jk7D6V+AI4DEzW2Nm/x50QEFKFtJ8GniURHHEA0pqh5wFfBw4J/leWZO8QpEypSs2ERGJFF2xiYhIpCixiYhIpCixiYhIpCixiYhIpCixiYhIpCixiYhIpCixiYhIpCixiZQAM5uT3Juu2sxGJfcdmxp0XCJhpAZtkRJhZreQWO+vBtjs7l8JOCSRUFJiEykRZjaCxBqR+4Ez3b0n4JBEQklTkSKlYxwwmsQamtUBxyISWrpiEykRZracxM7ZxwNHu/unAw5JJJS0H5tICTCzvwa63f0+M6sAfm1m57j7L4KOTSRsdMUmIiKRontsIiISKUpsIiISKUpsIiISKUpsIiISKUpsIiISKUpsIiISKUpsIiISKUpsIiISKf8LeYXsIIoCdJYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2 = plt.figure(figsize=(7,5))\n",
    "plt.plot(x,y,marker='o')\n",
    "plt.plot(x,y_predict,marker='o')\n",
    "plt.title('y vs x')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
